The growing widespread adoption of machine learning solutions in many industries and for many different types of analysis demands a universal, fast, and easy to use and understand system for model diagnostics that supports both novice and expert users. For example, despite the extraordinary success of using trained deep neural networks to model system behavior, there is a lack of a theoretical understanding of how a neural network is able to learn complicated patterns while generalizing and performing well on new data. The trained deep neural network acts as a black box tool whose properties are not well understood. High dimensionality of the parameter space and the input prevents effective visualization of the operation of the neural network. As a result, professionals and non-experts in the field both struggle to find a way to analyze operation of a given neural network model and to understand the drawbacks and the weaknesses in the given trained model. When a neural network model fails to perform as expected, the user finds it difficult to determine what caused the degraded performance and how to correct it.